Compressed-air energy storage power plant investments under uncertain electricity prices: an evaluation of compressed-air energy storage plants in liberalized energy markets

In this paper a combined approach is introduced, integrating electricity prices simulated with the help of a financial model into an optimization model that evaluates a compressed-air energy storage (CAES) plant investment. The financial model based on a regime-switching approach delivers suitable price paths. Based on these price paths, the optimization model maximizes the annual return of the CAES plant, taking bidding strategies on the spot and reserve power markets into account. A Monte Carlo simulation is carried out for the annual return, which, in turn, is used to determine the expected net present value (NPV). A negative NPV is obtained when applying an interest rate of 6%. Thus, the investment in current CAES plants is not attractive, as energy companies generally expect an annual yield over 8%.

[1]  C. Weber.  Uncertainty in the electric power industry , 2004 .

[2]  P. Mazur.  On the theory of brownian motion , 1959 .

[3]  D. Bunn,et al.  Evaluating the power investment options with uncertainty in climate policy , 2008 .

[4]  Jeffery B. Greenblatt,et al.  Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation , 2007 .

[5]  F. Nogales,et al.  Price-Taker Bidding Strategy under Price Uncertainty , 2002, IEEE Power Engineering Review.

[6]  O. Rentz,et al.  European electricity and emission market modeling — the design of emission allocation plans and its effects on investment planning , 2008, 2008 5th International Conference on the European Electricity Market.

[7]  Gianfranco Rizzo,et al.  Optimal Management of a Wind/CAES Power Plant by means of Neural Network Wind Speed Forecast , 2007 .

[8]  Manfred Steiner,et al.  Finanzwirtschaft der Unternehmung , 2012 .

[9]  C. Perrin,et al.  Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments , 2001 .

[10]  Brian Elmegaard,et al.  Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices , 2009 .

[11]  Thomas Muche,et al.  A real option-based simulation model to evaluate investments in pump storage plants , 2009 .

[12]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[13]  Dominik Möst,et al.  Zur Wettbewerbsfähigkeit der Wasserkraft in liberalisierten Elektrizitätsmärkten. Eine modellgestützte Analyse dargestellt am Beispiel des schweizerischen Energieversorgungssystems , 2006 .

[14]  Stephen L Taylor,et al.  Modelling Financial Time Series , 1987 .

[15]  Eduardo S. Schwartz,et al.  Electricity Prices and Power Derivatives: Evidence from the Nordic Power Exchange , 2000 .

[16]  M. Parsa Moghaddam,et al.  Electric energy storage systems in a market-based economy: Comparison of emerging and traditional technologies , 2009 .

[17]  George Crabtree,et al.  The hydrogen economy , 2006, IEEE Engineering Management Review.

[18]  P. Denholm,et al.  The value of compressed air energy storage in energy and reserve markets , 2011 .

[19]  Ning Lu,et al.  Pumped-storage hydro-turbine bidding strategies in a competitive electricity market , 2004, IEEE Transactions on Power Systems.

[20]  A. Chambers,et al.  World Energy Outlook 2008 , 2008 .

[21]  M. Davison,et al.  Natural gas storage valuation and optimization: A real options application , 2009 .

[22]  Neil Salkind,et al.  Encyclopedia of research design , 2010 .

[23]  D. Panescu,et al.  Emerging Technologies , 2008, IEEE Engineering in Medicine and Biology Magazine.

[24]  John K. Kaldellis,et al.  Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks , 2009 .

[25]  Septimus van der Linden,et al.  Bulk energy storage potential in the USA, current developments and future prospects , 2006 .

[26]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[27]  C. Huber-Carol.  Goodness-of-Fit Tests and Model Validity , 2012 .

[28]  G. Hirsch.  Pricing of hourly exercisable electricity swing options using different price processes , 2009 .

[29]  Cyriel de Jong,et al.  Cointegration between gas and power spot prices , 2009 .

[30]  Derek W. Bunn,et al.  Forecasting electricity prices: The impact of fundamentals and time-varying coefficients , 2008 .

[31]  R. Weron,et al.  Modelling Electricity Prices: Jump Diffusion and Regime Switching , 2004 .

[32]  C. Kemfert,et al.  A game theoretic model of the Northwestern European electricity market--market power and the environment , 2006 .

[33]  M. Uhrig-Homburg,et al.  Modelling Jumps in Electricity Prices: Theory and Empirical Evidence , 2006 .

[34]  Á. Cartea,et al.  Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality , 2005 .

[35]  Afzal S. Siddiqui,et al.  Valuing a gas-fired power plant: A comparison of ordinary linear models, regime-switching approaches, and models with stochastic volatility , 2010 .

[36]  Dominik Möst,et al.  A survey of stochastic modelling approaches for liberalised electricity markets , 2010, Eur. J. Oper. Res..